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TacticToe

swMATH ID: 28627
Software Authors: Gauthier, T., Kaliszyk, C., Urban, J.
Description: TacticToe: learning to reason with HOL4 tactics. Techniques combining machine learning with translation to automated reasoning have recently become an important component of formal proof assistants. Such ”hammer” tech- niques complement traditional proof assistant automation as implemented by tactics and decision procedures. In this paper we present a unified proof assistant automation approach which attempts to automate the selection of appropriate tactics and tactic-sequences com- bined with an optimized small-scale hammering approach. We implement the technique as a tactic-level automation for HOL4: TacticToe. It implements a modified A*-algorithm directly in HOL4 that explores different tactic-level proof paths, guiding their selection by learning from a large number of previous tactic-level proofs. Unlike the existing hammer methods, TacticToe avoids translation to FOL, working directly on the HOL level. By combining tactic prediction and premise selection, TacticToe is able to re-prove 39 percent of 7902 HOL4 theorems in 5 seconds whereas the best single HOL(y)Hammer strategy solves 32 percent in the same amount of time.
Homepage: https://easychair.org/publications/open/WsM
Related Software: E Theorem Prover; MaLeCoP; FEMaLeCoP; HOL; Coq; ENIGMA; VAMPIRE; OTTER; MaLARea; Mizar; Mace4; Prover9; MPTP 0.2; DeepMath; XGBoost; leanCoP; HOL Light; Isabelle; Isabelle/HOL; TPTP
Referenced in: 10 Publications

Referenced in 1 Serial

1 Journal of Automated Reasoning

Referenced in 1 Field

10 Computer science (68-XX)

Referencing Publications by Year